Searching Codebases

Find code in any codebase by pattern or concept. One entry point, two search strategies, automatic routing.

When NOT to use this skill

Python callers and definitions only. Every other code question is cheaper elsewhere, and the measurement below is why this skill is edge-case-only.

Situation	Use
What symbols does this file contain?	tree-sitting
Where is X defined, so I can read it?	tree-sitting
Any structure question, any language	tree-sitting
A literal string or regex	plain ripgrep
Which files are most about a concept?	bm25
First look at an unfamiliar repo	exploring-codebases

Non-Python code has no pyright binding resolution to offer, so there is no version of this skill that applies to it.

Prerequisites

uv tool install ripgrep

tree-sitting installs automatically when needed — for --expand context expansion and for the binding-resolved --refs/--def/--hover tier, which uses it to resolve symbol positions. Only the bare tree-sitter package is fetched; the language grammars ship bundled.

Primary Command

SKILL_DIR=/mnt/skills/user/searching-codebases

python3 $SKILL_DIR/scripts/search.py SOURCE "query1" ["query2" ...] [OPTIONS]

SOURCE is any of:

• Local directory path
• GitHub URL (downloads tarball automatically)
• uploads (uses /mnt/user-data/uploads/)
• project (uses /mnt/project/)
• Path to a .zip or .tar.gz archive

Search Modes

Regex mode (patterns, identifiers, literal text):

python3 $SKILL_DIR/scripts/search.py ./repo "def handle_error"
python3 $SKILL_DIR/scripts/search.py ./repo "class.*Exception" --regex
python3 $SKILL_DIR/scripts/search.py ./repo "TODO|FIXME|HACK"

Semantic mode (concepts, natural language):

python3 $SKILL_DIR/scripts/search.py ./repo "retry logic with backoff" --semantic
python3 $SKILL_DIR/scripts/search.py ./repo "authentication flow"
python3 $SKILL_DIR/scripts/search.py ./repo "error handling strategy"

Auto-detection: short queries and code-like tokens → regex. Multi-word natural language → semantic. Override with --regex or --semantic.

Binding-resolved mode (Python only — pyright via the python-lsp skill):

python3 $SKILL_DIR/scripts/search.py ./repo --refs SYMBOL    # find all real uses
python3 $SKILL_DIR/scripts/search.py ./repo --def SYMBOL     # go-to-definition
python3 $SKILL_DIR/scripts/search.py ./repo --hover SYMBOL   # inferred type/signature

Regex mode matches text, so a cross-reference for a function false-positives on shadowed and same-named-but-unrelated symbols. --refs is binding-resolved: pyright excludes the unrelated same-named symbol and follows imports. Use it when you need a true "find all callers/users" for a .py symbol, not a text grep.

The tier is engaged lazily — pyright's index cost is paid only when you ask for --refs/--def/--hover, never on ordinary searches. It is Python-only; for non-.py sources, or when pyright/node is unavailable, it prints a one-line degradation note and falls back to the regex text path. Each takes a single bare symbol name and is mutually exclusive with the other two and with text queries.

Options

• --regex / --semantic: Force search mode
• --refs SYMBOL / --def SYMBOL / --hover SYMBOL: Binding-resolved Python queries via pyright (see Binding-resolved mode above)
• --expand: Return full function bodies via tree-sitting AST context
• --benchmark: Compare indexed regex vs brute-force ripgrep
• --branch NAME: Git branch for GitHub URLs (default: main)
• --skip DIRS: Comma-separated directories to skip
• --json: Machine-readable output
• -v: Show index stats and query routing decisions

How It Works

Regex search builds a sparse n-gram inverted index over all files. Queries are decomposed into literal fragments, looked up in the index to identify candidate files (typically 90-99% reduction), then verified with ripgrep. Frequency-weighted n-grams make rare character sequences more selective.

Semantic search builds a TF-IDF index over code chunks (functions, classes, structural entries). Queries are ranked by cosine similarity.

Context expansion (--expand) uses tree-sitting's AST cache to identify function/class boundaries, returning complete structural units rather than line fragments. On first use, tree-sitting scans the repo (~700ms for 250 files); subsequent expansions are sub-millisecond.

Small codebases (< 20 files) skip indexing entirely — direct ripgrep is faster when there's nothing to narrow.

Mixed Queries

Multiple queries can use different modes in a single invocation. Each query is auto-routed independently, and indexes are built once per mode:

python3 $SKILL_DIR/scripts/search.py ./repo \
  "class.*Error" \
  "error recovery strategy" \
  "def retry"

Dependencies

• tree-sitting: Provides AST context expansion for --expand and the symbol→position resolution that seeds the binding-resolved tier (--refs/--def/--hover). Auto-installs the bare tree-sitter package when either is used (grammars are bundled). Regex and semantic search work without it.
• ripgrep: Required for regex verification. Install via uv tool install ripgrep.
• scikit-learn: Required for semantic mode. Installs automatically.
• python-lsp: Provides the binding-resolved tier (--refs/--def/--hover). Self-bootstraps pyright on first use and requires system node (v18+). Not required — without it those flags degrade to the regex text path.

When to Use — narrow, by design

The ONE recommended use: binding-resolved Python symbol queries.

• "find all callers of X" / "where is X really defined" for a .py symbol, when same-named-but-unrelated symbols would pollute a text grep. Empirical basis: rg get on psf/requests returned 232 hits, 224 of them false; --refs get excluded all 224 (2026-06-15).

When NOT to Use — which is most of the time

Everything else. Measured head-to-head on real issue-localization tasks (7 scikit-learn issues with merged fix-PRs, gold = PR diff files, 2026-07-04, replicating the file-discovery metric of arXiv:2602.11988):

• Literal tokens / identifiers: naive rg -l tied or beat the indexed tier on recall@10 in every instance, at 0.4s vs 25s.
• Concept / natural-language search: the TF-IDF semantic tier never beat identifier grep — not even on identifier-poor issues, which are themselves rare (~0.3% of merged-PR traffic in the sample).
• First encounter / "what is this repo": use exploring-codebases.
• Repos under ~20 files: read them.

The self-test before invoking: would plain rg return the same answer? If yes, use rg. The indexed-regex and semantic tiers are retained for completeness and for corpora where they may yet earn their cost (very large repos, non-code document collections), but they carry the burden of proof.

Files

• scripts/search.py — Entry point, query routing, output formatting
• scripts/resolve.py — Input source resolution (GitHub, uploads, archives)
• scripts/context.py — tree-sitting-based AST context expansion
• scripts/ngram_index.py — Sparse n-gram inverted index, regex decomposition
• scripts/sparse_ngrams.py — Core n-gram algorithms, frequency weights
• scripts/code_rag.py — TF-IDF semantic search over code chunks
• scripts/lsp_refs.py — Binding-resolved Python tier: symbol→position resolution (tree-sitting), pyright queries (python-lsp), soft fallback